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Enabling Analysis of Big, Thick, Long, and Wide Data: Data Management for the Analysis of a Large Longitudinal and Cross-National Narrative Data Set.

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Title: Enabling Analysis of Big, Thick, Long, and Wide Data: Data Management for the Analysis of a Large Longitudinal and Cross-National Narrative Data Set.
Authors: Winskell, Kate1 swinske@emory.edu, Singleton, Robyn1, Sabben, Gaelle1
Source: Qualitative Health Research. Aug2018, Vol. 28 Issue 10, p1629-1639. 11p.
Subject Terms: *Culture, *Database management, *Longitudinal method, *Research methodology, HIV prevention, Grounded theory, Research funding, Statistical sampling, Narratives, Data analysis software, Descriptive statistics
Geographic Terms: Sub-Saharan Africa
Abstract: Distinctive longitudinal narrative data, collected during a critical 18-year period in the history of the HIV epidemic, offer a unique opportunity to examine how young Africans are making sense of evolving developments in HIV prevention and treatment. More than 200,000 young people from across sub-Saharan Africa took part in HIV-themed scriptwriting contests held at eight discrete time points between 1997 and 2014, creating more than 75,000 narratives. This article describes the data reduction and management strategies developed for our cross-national and longitudinal study of these qualitative data. The study aims to inform HIV communication practice by identifying cultural meanings and contextual factors that inform sexual behaviors and social practices, and also to help increase understanding of processes of sociocultural change. We describe our sampling strategies and our triangulating methodologies, combining in-depth narrative analysis, thematic qualitative analysis, and quantitative analysis, which are designed to enable systematic comparison without sacrificing ethnographic richness. [ABSTRACT FROM AUTHOR]
Copyright of Qualitative Health Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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PubType: Academic Journal
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  Data: Enabling Analysis of Big, Thick, Long, and Wide Data: Data Management for the Analysis of a Large Longitudinal and Cross-National Narrative Data Set.
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  Data: Distinctive longitudinal narrative data, collected during a critical 18-year period in the history of the HIV epidemic, offer a unique opportunity to examine how young Africans are making sense of evolving developments in HIV prevention and treatment. More than 200,000 young people from across sub-Saharan Africa took part in HIV-themed scriptwriting contests held at eight discrete time points between 1997 and 2014, creating more than 75,000 narratives. This article describes the data reduction and management strategies developed for our cross-national and longitudinal study of these qualitative data. The study aims to inform HIV communication practice by identifying cultural meanings and contextual factors that inform sexual behaviors and social practices, and also to help increase understanding of processes of sociocultural change. We describe our sampling strategies and our triangulating methodologies, combining in-depth narrative analysis, thematic qualitative analysis, and quantitative analysis, which are designed to enable systematic comparison without sacrificing ethnographic richness. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Qualitative Health Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/1049732318759658
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      – SubjectFull: Narratives
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      – SubjectFull: Data analysis software
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Sub-Saharan Africa
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      – TitleFull: Enabling Analysis of Big, Thick, Long, and Wide Data: Data Management for the Analysis of a Large Longitudinal and Cross-National Narrative Data Set.
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              Text: Aug2018
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